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Kohl's is a U.S. retail company combining a nationwide store network with an online shopping platform. Its assortment spans apparel, home goods, beauty, and other everyday merchandise, including Sephora beauty offerings through its partnership with Sephora at Kohl’s. The company’s operations extend beyond customer-facing retail to distribution centers, credit and call center teams, and design and corporate functions. Kohl's hiring context can therefore include opportunities supporting omnichannel commerce, store operations, fulfillment, customer service, and corporate retail teams, with a stated focus on customer-first service and an inclusive workplace culture.
Machine Learning Operations Manager at Kohl's, Menomonee Falls Onsite
Lead ML Operations engineers supporting scalable machine-learning infrastructure for Kohl’s retail organization. Oversee production model deployment, cloud tooling, and machine-learning lifecycle practices.
Description
- Build and lead the ML Operations engineering team through hiring, development, coaching, management, and performance assessment.
- Guide implementation of the complete machine-learning lifecycle, including scalable ETL pipelines, production deployment of Data Science models, and cloud infrastructure and tooling.
- Set the roadmap for Machine Learning Engineering and Data Science tools by creating reusable frameworks and standardized implementation solutions.
- Investigate and prototype advanced machine-learning infrastructure, then guide the team in applying it.
- Accelerate team execution by resolving blockers and advancing decisions.
- Advise Data Science managers on cloud tools and infrastructure supporting the machine-learning lifecycle.
- Establish engineering best practices and promote ongoing development through training, coaching, pair programming, and code reviews.
- Help build monitoring, alerting, development, and automated testing frameworks that protect the reliability, performance, and integrity of pipelines, models, and infrastructure.
- Record and share implementations and best practices with leaders throughout Kohl’s data organization.
- Track developments in Google Cloud Platform services and current MLE and ML Operations practices.
- Complete other assigned responsibilities.
Requirements
- Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, or a comparable quantitative discipline.
- At least five years of Machine Learning Engineering experience with a record of independently delivering projects successfully.
- At least two years of management or leadership experience in Data Science or Analytics organizations.
- Advanced ML Operations expertise, including production ML systems, Docker, Kubernetes, CI/CD, Git-based version control, API development, batch and real-time model serving, and automated testing.
- Experience partnering with Data Scientists to deploy, scale, and operationalize production machine-learning models.
- Deep knowledge of cloud platforms, preferably Google Cloud Platform, including Vertex AI, BigQuery, and Dataproc.
- Extensive knowledge of CI/CD and infrastructure-as-code practices.
- Strong command of distributed computing and big-data technologies such as Spark, Kubeflow, Airflow, and SQL.
- Advanced Python skills and experience with machine-learning libraries including TensorFlow, PyTorch, and scikit-learn.
- Experience in Agile environments focused on iterative delivery and continuous development.
- Proficiency in Java or another programming language.
- Experience in retail and e-commerce.
- Experience using optimization methods and tools such as Gurobi, linear programming, and mixed-integer programming.
- Experience with agent-based or agentic AI systems, including orchestration of autonomous workflows or LLM-driven agents.
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